Parameterization of disorder predictors for large-scale applications requiring high specificity by using an extended benchmark dataset

Abstract

This work identifies parameter settings and thresholds for a selection of disorder predictors to produce comparable results at a desired level of specificity over a newly derived benchmark dataset that accounts equally for ordered and disordered regions of different lengths.